Is the Fed’s news perception different from the private sector’s?
Gabriela Best () and
Applied Economics, 2019, vol. 51, issue 16, 1694-1710
The recent literature on monetary policy has dedicated considerable attention to modelling agents’ processing of information about the future in real time. This paper contributes to this growing strand by investigating the implied differences in the so-called news shocks estimated from the standard New Keynesian dynamic stochastic general equilibrium (DSGE) model using the real-time data sets from the Survey of Professional Forecasters (SPF) and the Federal Reserve’s Greenbook (GB) forecasts. Alternative specifications with either the SPF or GB forecasts aim to delineate the differences in the private sector’s and the Fed’s expectations of future macroeconomic outcomes and identify the differences in their perception of news shocks. Our results indicate that while the demand news shocks have very similar distributions in the two datasets, the monetary and cost-push news shocks from the models estimated on the GB data tend to be larger than those from the SPF. These findings suggest that the Federal Reserve’s forecasting methods allow for more variation in future outcomes than the SPF’s. These findings mesh well with the extant literature on the superiority of the Fed’s forecasts relative to the private sector’s and provide a structural explanation for the source of this superiority.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:51:y:2019:i:16:p:1694-1710
Ordering information: This journal article can be ordered from
Access Statistics for this article
Applied Economics is currently edited by Anita Phillips
More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().